An Optimum-Path Forest framework for intrusion detection in computer networks

Engineering Applications of Artificial Intelligence

Volumn 25, Issue 6, 2012, Pages 1226-1234

  Pereira, Clayton R ^a   Nakamura, Rodrigo Y M ^a   Costa, Kelton A P ^a   Papa, João P ^a  

^a SÃO PAULO STATE UNIVERSITY UNESP   (Brazil)

Author keywords

Computer security; Intrusion detection system; Machine learning; Optimum Path Forest

Indexed keywords

ARTIFICIAL INTELLIGENCE TECHNIQUES; BAYESIAN CLASSIFIER; DATA SETS; INTRUSION DETECTION SYSTEMS; OPTIMUM-PATH FORESTS; PATTERN RECOGNITION TECHNIQUES; REAL TIME;

ARTIFICIAL INTELLIGENCE; COMPUTER CRIME; FORESTRY; LEARNING SYSTEMS; PATTERN RECOGNITION; SECURITY OF DATA; SELF ORGANIZING MAPS;

INTRUSION DETECTION;

ARTIFICIAL INTELLIGENCE; DATA BASES; FORESTS; PATTERN RECOGNITION;

EID: 84864563699     PISSN: 09521976     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.engappai.2012.03.008     Document Type: Article

References (41)

1. Allène, C.; Audibert, J.; Couprie, M.; Cousty, J.; Keriven, R.; 2007. Some links between min-cuts, optimal spanning forests and watersheds. In: Proceedings of the 8th International Symposium on Mathematical Morphology, pp. 253-264.
2. Cannady, J.; 1998. Artificial neural networks for misuse detection. In: National Information Systems Security Conference, pp. 443-456.
3. S. Chabaa, A. Zeroual, and J. Antari Identification and prediction of internet traffic using artificial neural networks J. Intell. Learn. Syst. Appl. 2 3 2010 147 155
4. W.H. Chen, S.H. Hsu, and H.P. Shen Application of SVM and ANN for intrusion detection Comput. Oper. Res. 32 10 2005 2617 2634
5. R. Collobert, and S. Bengio SVMTorch: support vector machines for large-scale regression problems J. Mach. Learn. Res. 1 2001 143 160 (Pubitemid 33738779)
6. C. Cortes, and V. Vapnik Support vector networks Mach. Learn. 20 1995 273 297
7. E. Dijkstra A note on two problems in connexion with graphs Numer. Math. 1 1959 269 271
8. A.X. Falcão, J. Stolfi, and R.A. Lotufo The image foresting transform: theory, algorithms, and applications IEEE Trans. Pattern Anal. Mach. Intell. 26 1 2004 19 29
9. Geem, Z.W.; 2009. Music-Inspired Harmony Search Algorithm: Theory and Applications, first ed.; Springer Publishing Company, Incorporated.
10. Ghosh, A.K.; Wanken, J.; Charron, F.; 1998. Detecting anomalous and unknown intrusions against programs. In: Proceedings of the Annual Computer Security Application Conference, pp. 259-267.
11. Haijun, X.; Fang, P.; Ling, W.; Hongwei, L.; 2007. Ad hoc-based feature selection and support vector machine classifier for intrusion detection. In: Proceedings of IEEE International Conference on Grey Systems and Intelligent Services, pp. 18-20.
12. D. Halliday, R. Resnick, J. Walker, D. Halliday, R. Resnick, and J. Walker Extended, Fundamentals of Physics sixth ed. 2000 Wiley
13. S. Haykin Neural Networks: A Comprehensive Foundation 1998 Prentice-Hall
14. W. Jia, B. Ling, K.-W. Chau, and L. Heutte Palmprint identification using restricted fusion Appl. Math. Comput. 205 2 2008 927 934
15. I. Jolliffe Principal Component Analysis Springer Series in Statistics second ed. 2002 Springer
16. Jucá, K.R.L.; Boukerche, A.; Sobral, J.B.M.; 2002. Intrusion detection based on the immune human system. in: Proceedings of the 16th International Parallel and Distributed Processing Symposium, IEEE Computer Society, Washington, DC, USA, p. 187.
17. Kang, D.-K.; Fuller, D.; Honavar, V.; 2005. Learning classifiers for misuse detection using a bag of system calls representation. In: Proceedings of IEEE International Conference on Intelligence and Security Informatics. Lecture Notes in Computer Science, vol. 3495, Springer-Verlag, Berlin, Heidelberg, pp. 511-516. (Pubitemid 41314466)
18. H.G. Kayacik, A.N.Z. Heywood, and I.M. Heywood A hierarchical SOM-based intrusion detection system Eng. Appl. Artif Intell. 20 4 2007 439 451
19. J. Kennedy, and R. Eberhart Swarm Intelligence 2001 M. Kaufman
20. Lei, J.Z.; Ghorbani, A.A.; 2004. Network intrusion detection using an improved competitive learning neural network. In: Proceedings of the 2nd Annual Conference on Communication Networks and Services Research, IEEE Computer Society, pp. 190-197.
21. Li, H.; Gu, D.; 2009. A novel intrusion detection scheme using support vector machine fuzzy network for mobile ad hoc networks. In: Pacific-Asia Conference on Web Mining and Web-based Application, pp. 47-50.
22. J.-Y. Lin, C.-T. Cheng, and K.-W. Chau Using support vector machines for long-term discharge prediction Hydrol. Sci. J. 51 4 2006 599 612 (Pubitemid 44178040)
23. Papa, J.P.; Falcão, A.X.; 2008. A new variant of the optimum-path forest classifier. In: Proceeding of the 4th International Symposium on Advances in Visual Computing, Springer-Verlag, Berlin, Heidelberg, pp. 935-944.
24. J.P. Papa, A.X. Falcão, and C.T.N. Suzuki Supervised pattern classification based on optimum-path forest Int. J. Imaging Syst. Technol. 19 2 2009 120 131
25. Papa, J.P.; Suzuki, C.T.N.; Falcão, A.X.; 2009b. LibOPF: a library for the design of optimum-path forest classifiers, software version 2.0 available at 〈 http://www.ic.unicamp.br/∼afalcao/LibOPF 〉.
26. J.P. Papa, A.X. Falcão, G.M. de Freitas, and A.M.H. de Ávila Robust pruning of training patterns for optimum-path forest classification applied to satellite-based rainfall occurrence estimation IEEE Geosci. Remote Sensing Lett. 7 2 2010 396 400
27. J.P. Papa, A.X. Falcão, V.H.C. Albuquerque, and J.M.R.S. Tavares Efficient supervised optimum-path forest classification for large datasets Pattern Recognition 45 1 2012 512 520
28. Ramos, C.C.O.; Souza, A.N.; Falcão, A.X.; Papa, J.P.; New insights on non-technical losses characterization through evolutionary-based feature selection, IEEE Trans. Power Delivery 27 (1), 140-146.
29. Ramos, C.C.O.; Souza, A.N.; Chiachia, G.; Falcão, A.X.; Papa, J.P.; A novel algorithm for feature selection using harmony search and its application for non-technical losses detection, Comput. Electrical Eng. 37 (6), 886-894.
30. Ramos, C.C.O.; Papa, J.P.; Souza, A.N.; Chiachia, G.; Falcão, A.X.; 2011. What is the importance of selecting features for non-technical losses identification? In: Proceedings of the IEEE International Symposium on Circuits and Systems, Rio de Janeiro, Brazil, pp. 1045-1048.
31. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi GSA: a gravitational search algorithm Inf. Sci. 179 13 2009 2232 2248
32. L.M. Rocha, F.A.M. Cappabianco, and A.X. Falcão Data clustering as an optimum-path forest problem with applications in image analysis Int. J. Imaging Syst. Technol. 19 2 2009 50 68
33. Shyu, M.L.; Chen, S.C.; Sarinnapakorn, K.; Chang, L.; 2003. A novel anomaly detection scheme based on principal component classifier. In: Proceedings of the IEEE Foundations and New Directions of Data Mining Workshop, pp. 172-179.
34. S. Sindhu, S. Geetha, and A. Kannan Decision tree based light weight intrusion detection using a wrapper approach Expert Syst. Appl. 39 1 2012 129 141
35. Stein, G.; Chen, B.; Wu, A.S.; Hua, K.A.; 2005. Decision tree classifier for network intrusion detection with ga-based feature selection. In: Proceedings of the 43rd Annual Southeast Regional Conference, vol. 2. ACM-SE 43, ACM, New York, NY, USA, pp. 136-141.
36. Tavallaee, M.; Bagheri, E.; Lu, W.; Ghorbani, A.A.; 2009. A detailed analysis of the kdd cup 99 data set. In: Proceedings of the Second IEEE International Conference on Computational Intelligence for Security and Defense Applications, IEEE Press, Piscataway, NJ, USA, pp. 53-58.
37. W. Wang, and W. Wu Online detection of network traffic anomalies using degree distributions Int. J. Commun. Network Syst. Sci. 3 2 2010 177 182
38. S. Wu, and W. Banzhaf The use of computational intelligence in intrusion detection systems: a review Appl. Soft Comput. 10 1 2010 1 35
39. Zanero, S.; Savaresi, S.M.; 2004. Unsupervised learning techniques for an intrusion detection system. In: Proceedings of the 2004 ACM Symposium on Applied Computing, ACM, New York, USA, pp. 412-419.
40. J. Zhang, and K.-W. Chau Multilayer ensemble pruning via novel multi-sub-swarm particle swarm optimization J. Univer. Comput. Sci. 15 4 2009 840 858
41. Zhang, H.; Wang, X.; 2008. Random subspace PCA based intrusion detection classifier ensemble. In: Proceedings of the 7th World Congress on Intelligent Control and Automation, pp. 3140 -3144.